In this work, given a pair of bags (a positive bag \(B^a\)  and a negative bag \(B^n\) ), we first pre-extract the features (\(\lbrace \mathbf {f}^a_i\rbrace _{i=1}^N\)  and \(\lbrace \mathbf {f}^n_i\rbrace _{i=1}^N\)  for \(B^a\)  and \(B^n\) , respectively) for each clip in the video \(V=\lbrace v_i\rbrace ^N_{i=1}\)  using a pretrained vanilla feature encoder[rgb]0,0,0, C3D or I3D, forming bags of features \(\overline{B}^a\)  and \(\overline{B}^n\) . We then feed the pseudo label generator the extracted features to estimate the anomaly scores of the clips (\(\lbrace s_i^a\rbrace ^N_{i=1}\) , \(\lbrace s_i^n\rbrace ^N_{i=1}\) ).
Then, we produce pseudo labels \(\hat{Y}^a=\lbrace \hat{y}^a_i\rbrace _{i=1}^N\)  for anomalous video by performing smoothing and normalization on estimated scores to supervise the learning of the [rgb]0.,0,0proposed self-guided attention boosted feature encoder, [rgb]0.,0.,0forming as two-stage self-training scheme [1]}, [2]}, [3]}.
